{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.880408\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -18.148679 analytic: -18.040086, relative error: 3.000741e-03\n",
      "numerical: 30.927717 analytic: 30.927717, relative error: 1.008135e-11\n",
      "numerical: 24.635175 analytic: 24.635175, relative error: 6.561766e-12\n",
      "numerical: -4.900125 analytic: -4.863407, relative error: 3.760764e-03\n",
      "numerical: -5.417848 analytic: -5.417848, relative error: 4.664871e-12\n",
      "numerical: 18.358488 analytic: 18.358488, relative error: 2.251292e-11\n",
      "numerical: -27.238972 analytic: -27.150744, relative error: 1.622150e-03\n",
      "numerical: 17.118388 analytic: 17.118388, relative error: 3.711815e-13\n",
      "numerical: 23.382409 analytic: 23.382409, relative error: 4.902939e-12\n",
      "numerical: 16.902015 analytic: 16.902015, relative error: 4.440048e-12\n",
      "numerical: -34.814739 analytic: -34.675854, relative error: 1.998617e-03\n",
      "numerical: 15.551802 analytic: 15.551802, relative error: 1.595149e-11\n",
      "numerical: 21.600851 analytic: 21.548203, relative error: 1.220141e-03\n",
      "numerical: 29.931542 analytic: 29.931542, relative error: 9.689934e-12\n",
      "numerical: 24.450212 analytic: 24.450212, relative error: 6.945677e-12\n",
      "numerical: -3.040207 analytic: -3.040207, relative error: 7.844800e-12\n",
      "numerical: 17.778312 analytic: 17.773536, relative error: 1.343373e-04\n",
      "numerical: 14.523925 analytic: 14.502368, relative error: 7.426872e-04\n",
      "numerical: -2.479108 analytic: -2.479108, relative error: 2.388528e-11\n",
      "numerical: -37.377634 analytic: -37.312628, relative error: 8.703317e-04\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *fill this in*"
   ]
  }
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